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libcamera: ipa: Raspberry Pi IPA

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Initial implementation of the Raspberry Pi (BCM2835) libcamera IPA and
associated libraries.

All code is licensed under the BSD-2-Clause terms.
Copyright (c) 2019-2020 Raspberry Pi Trading Ltd.

Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
Acked-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
This commit is contained in:
Naushir Patuck 2020-05-03 16:48:42 +01:00 • committed by Laurent Pinchart
parent 740fd1b62f
commit 0db2c8dc75
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src/ipa/raspberrypi/controller/rpi/alsc.cpp Normal file
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi (Trading) Limited
*
* alsc.cpp - ALSC (auto lens shading correction) control algorithm
*/
#include <math.h>
#include "../awb_status.h"
#include "alsc.hpp"
// Raspberry Pi ALSC (Auto Lens Shading Correction) algorithm.
using namespace RPi;
#define NAME "rpi.alsc"
static const int X = ALSC_CELLS_X;
static const int Y = ALSC_CELLS_Y;
static const int XY = X * Y;
static const double INSUFFICIENT_DATA = -1.0;
Alsc::Alsc(Controller *controller)
: Algorithm(controller)
{
async_abort_ = async_start_ = async_started_ = async_finished_ = false;
async_thread_ = std::thread(std::bind(&Alsc::asyncFunc, this));
}
Alsc::~Alsc()
{
{
std::lock_guard<std::mutex> lock(mutex_);
async_abort_ = true;
async_signal_.notify_one();
}
async_thread_.join();
}
char const *Alsc::Name() const
{
return NAME;
}
static void generate_lut(double *lut, boost::property_tree::ptree const &params)
{
double cstrength = params.get<double>("corner_strength", 2.0);
if (cstrength <= 1.0)
throw std::runtime_error("Alsc: corner_strength must be > 1.0");
double asymmetry = params.get<double>("asymmetry", 1.0);
if (asymmetry < 0)
throw std::runtime_error("Alsc: asymmetry must be >= 0");
double f1 = cstrength - 1, f2 = 1 + sqrt(cstrength);
double R2 = X * Y / 4 * (1 + asymmetry * asymmetry);
int num = 0;
for (int y = 0; y < Y; y++) {
for (int x = 0; x < X; x++) {
double dy = y - Y / 2 + 0.5,
dx = (x - X / 2 + 0.5) * asymmetry;
double r2 = (dx * dx + dy * dy) / R2;
lut[num++] =
(f1 * r2 + f2) * (f1 * r2 + f2) /
(f2 * f2); // this reproduces the cos^4 rule
}
}
}
static void read_lut(double *lut, boost::property_tree::ptree const &params)
{
int num = 0;
const int max_num = XY;
for (auto &p : params) {
if (num == max_num)
throw std::runtime_error(
"Alsc: too many entries in LSC table");
lut[num++] = p.second.get_value<double>();
}
if (num < max_num)
throw std::runtime_error("Alsc: too few entries in LSC table");
}
static void read_calibrations(std::vector<AlscCalibration> &calibrations,
boost::property_tree::ptree const &params,
std::string const &name)
{
if (params.get_child_optional(name)) {
double last_ct = 0;
for (auto &p : params.get_child(name)) {
double ct = p.second.get<double>("ct");
if (ct <= last_ct)
throw std::runtime_error(
"Alsc: entries in " + name +
" must be in increasing ct order");
AlscCalibration calibration;
calibration.ct = last_ct = ct;
boost::property_tree::ptree const &table =
p.second.get_child("table");
int num = 0;
for (auto it = table.begin(); it != table.end(); it++) {
if (num == XY)
throw std::runtime_error(
"Alsc: too many values for ct " +
std::to_string(ct) + " in " +
name);
calibration.table[num++] =
it->second.get_value<double>();
}
if (num != XY)
throw std::runtime_error(
"Alsc: too few values for ct " +
std::to_string(ct) + " in " + name);
calibrations.push_back(calibration);
RPI_LOG("Read " << name << " calibration for ct "
<< ct);
}
}
}
void Alsc::Read(boost::property_tree::ptree const &params)
{
RPI_LOG("Alsc");
config_.frame_period = params.get<uint16_t>("frame_period", 12);
config_.startup_frames = params.get<uint16_t>("startup_frames", 10);
config_.speed = params.get<double>("speed", 0.05);
double sigma = params.get<double>("sigma", 0.01);
config_.sigma_Cr = params.get<double>("sigma_Cr", sigma);
config_.sigma_Cb = params.get<double>("sigma_Cb", sigma);
config_.min_count = params.get<double>("min_count", 10.0);
config_.min_G = params.get<uint16_t>("min_G", 50);
config_.omega = params.get<double>("omega", 1.3);
config_.n_iter = params.get<uint32_t>("n_iter", X + Y);
config_.luminance_strength =
params.get<double>("luminance_strength", 1.0);
for (int i = 0; i < XY; i++)
config_.luminance_lut[i] = 1.0;
if (params.get_child_optional("corner_strength"))
generate_lut(config_.luminance_lut, params);
else if (params.get_child_optional("luminance_lut"))
read_lut(config_.luminance_lut,
params.get_child("luminance_lut"));
else
RPI_WARN("Alsc: no luminance table - assume unity everywhere");
read_calibrations(config_.calibrations_Cr, params, "calibrations_Cr");
read_calibrations(config_.calibrations_Cb, params, "calibrations_Cb");
config_.default_ct = params.get<double>("default_ct", 4500.0);
config_.threshold = params.get<double>("threshold", 1e-3);
}
static void get_cal_table(double ct,
std::vector<AlscCalibration> const &calibrations,
double cal_table[XY]);
static void resample_cal_table(double const cal_table_in[XY],
CameraMode const &camera_mode,
double cal_table_out[XY]);
static void compensate_lambdas_for_cal(double const cal_table[XY],
double const old_lambdas[XY],
double new_lambdas[XY]);
static void add_luminance_to_tables(double results[3][Y][X],
double const lambda_r[XY], double lambda_g,
double const lambda_b[XY],
double const luminance_lut[XY],
double luminance_strength);
void Alsc::Initialise()
{
RPI_LOG("Alsc");
frame_count2_ = frame_count_ = frame_phase_ = 0;
first_time_ = true;
// Initialise the lambdas. Each call to Process then restarts from the
// previous results. Also initialise the previous frame tables to the
// same harmless values.
for (int i = 0; i < XY; i++)
lambda_r_[i] = lambda_b_[i] = 1.0;
}
void Alsc::SwitchMode(CameraMode const &camera_mode)
{
// There's a bit of a question what we should do if the "crop" of the
// camera mode has changed. Any calculation currently in flight would
// not be useful to the new mode, so arguably we should abort it, and
// generate a new table (like the "first_time" code already here). When
// the crop doesn't change, we can presumably just leave things
// alone. For now, I think we'll just wait and see. When the crop does
// change, any effects should be transient, and if they're not transient
// enough, we'll revisit the question then.
camera_mode_ = camera_mode;
if (first_time_) {
// On the first time, arrange for something sensible in the
// initial tables. Construct the tables for some default colour
// temperature. This echoes the code in doAlsc, without the
// adaptive algorithm.
double cal_table_r[XY], cal_table_b[XY], cal_table_tmp[XY];
get_cal_table(4000, config_.calibrations_Cr, cal_table_tmp);
resample_cal_table(cal_table_tmp, camera_mode_, cal_table_r);
get_cal_table(4000, config_.calibrations_Cb, cal_table_tmp);
resample_cal_table(cal_table_tmp, camera_mode_, cal_table_b);
compensate_lambdas_for_cal(cal_table_r, lambda_r_,
async_lambda_r_);
compensate_lambdas_for_cal(cal_table_b, lambda_b_,
async_lambda_b_);
add_luminance_to_tables(sync_results_, async_lambda_r_, 1.0,
async_lambda_b_, config_.luminance_lut,
config_.luminance_strength);
memcpy(prev_sync_results_, sync_results_,
sizeof(prev_sync_results_));
first_time_ = false;
}
}
void Alsc::fetchAsyncResults()
{
RPI_LOG("Fetch ALSC results");
async_finished_ = false;
async_started_ = false;
memcpy(sync_results_, async_results_, sizeof(sync_results_));
}
static double get_ct(Metadata *metadata, double default_ct)
{
AwbStatus awb_status;
awb_status.temperature_K = default_ct; // in case nothing found
if (metadata->Get("awb.status", awb_status) != 0)
RPI_WARN("Alsc: no AWB results found, using "
<< awb_status.temperature_K);
else
RPI_LOG("Alsc: AWB results found, using "
<< awb_status.temperature_K);
return awb_status.temperature_K;
}
static void copy_stats(bcm2835_isp_stats_region regions[XY], StatisticsPtr &stats,
AlscStatus const &status)
{
bcm2835_isp_stats_region *input_regions = stats->awb_stats;
double *r_table = (double *)status.r;
double *g_table = (double *)status.g;
double *b_table = (double *)status.b;
for (int i = 0; i < XY; i++) {
regions[i].r_sum = input_regions[i].r_sum / r_table[i];
regions[i].g_sum = input_regions[i].g_sum / g_table[i];
regions[i].b_sum = input_regions[i].b_sum / b_table[i];
regions[i].counted = input_regions[i].counted;
// (don't care about the uncounted value)
}
}
void Alsc::restartAsync(StatisticsPtr &stats, Metadata *image_metadata)
{
RPI_LOG("Starting ALSC thread");
// Get the current colour temperature. It's all we need from the
// metadata.
ct_ = get_ct(image_metadata, config_.default_ct);
// We have to copy the statistics here, dividing out our best guess of
// the LSC table that the pipeline applied to them.
AlscStatus alsc_status;
if (image_metadata->Get("alsc.status", alsc_status) != 0) {
RPI_WARN("No ALSC status found for applied gains!");
for (int y = 0; y < Y; y++)
for (int x = 0; x < X; x++) {
alsc_status.r[y][x] = 1.0;
alsc_status.g[y][x] = 1.0;
alsc_status.b[y][x] = 1.0;
}
}
copy_stats(statistics_, stats, alsc_status);
frame_phase_ = 0;
// copy the camera mode so it won't change during the calculations
async_camera_mode_ = camera_mode_;
async_start_ = true;
async_started_ = true;
async_signal_.notify_one();
}
void Alsc::Prepare(Metadata *image_metadata)
{
// Count frames since we started, and since we last poked the async
// thread.
if (frame_count_ < (int)config_.startup_frames)
frame_count_++;
double speed = frame_count_ < (int)config_.startup_frames
? 1.0
: config_.speed;
RPI_LOG("Alsc: frame_count " << frame_count_ << " speed " << speed);
{
std::unique_lock<std::mutex> lock(mutex_);
if (async_started_ && async_finished_) {
RPI_LOG("ALSC thread finished");
fetchAsyncResults();
}
}
// Apply IIR filter to results and program into the pipeline.
double *ptr = (double *)sync_results_,
*pptr = (double *)prev_sync_results_;
for (unsigned int i = 0;
i < sizeof(sync_results_) / sizeof(double); i++)
pptr[i] = speed * ptr[i] + (1.0 - speed) * pptr[i];
// Put output values into status metadata.
AlscStatus status;
memcpy(status.r, prev_sync_results_[0], sizeof(status.r));
memcpy(status.g, prev_sync_results_[1], sizeof(status.g));
memcpy(status.b, prev_sync_results_[2], sizeof(status.b));
image_metadata->Set("alsc.status", status);
}
void Alsc::Process(StatisticsPtr &stats, Metadata *image_metadata)
{
// Count frames since we started, and since we last poked the async
// thread.
if (frame_phase_ < (int)config_.frame_period)
frame_phase_++;
if (frame_count2_ < (int)config_.startup_frames)
frame_count2_++;
RPI_LOG("Alsc: frame_phase " << frame_phase_);
if (frame_phase_ >= (int)config_.frame_period ||
frame_count2_ < (int)config_.startup_frames) {
std::unique_lock<std::mutex> lock(mutex_);
if (async_started_ == false) {
RPI_LOG("ALSC thread starting");
restartAsync(stats, image_metadata);
}
}
}
void Alsc::asyncFunc()
{
while (true) {
{
std::unique_lock<std::mutex> lock(mutex_);
async_signal_.wait(lock, [&] {
return async_start_ || async_abort_;
});
async_start_ = false;
if (async_abort_)
break;
}
doAlsc();
{
std::lock_guard<std::mutex> lock(mutex_);
async_finished_ = true;
sync_signal_.notify_one();
}
}
}
void get_cal_table(double ct, std::vector<AlscCalibration> const &calibrations,
double cal_table[XY])
{
if (calibrations.empty()) {
for (int i = 0; i < XY; i++)
cal_table[i] = 1.0;
RPI_LOG("Alsc: no calibrations found");
} else if (ct <= calibrations.front().ct) {
memcpy(cal_table, calibrations.front().table,
XY * sizeof(double));
RPI_LOG("Alsc: using calibration for "
<< calibrations.front().ct);
} else if (ct >= calibrations.back().ct) {
memcpy(cal_table, calibrations.back().table,
XY * sizeof(double));
RPI_LOG("Alsc: using calibration for "
<< calibrations.front().ct);
} else {
int idx = 0;
while (ct > calibrations[idx + 1].ct)
idx++;
double ct0 = calibrations[idx].ct,
ct1 = calibrations[idx + 1].ct;
RPI_LOG("Alsc: ct is " << ct << ", interpolating between "
<< ct0 << " and " << ct1);
for (int i = 0; i < XY; i++)
cal_table[i] =
(calibrations[idx].table[i] * (ct1 - ct) +
calibrations[idx + 1].table[i] * (ct - ct0)) /
(ct1 - ct0);
}
}
void resample_cal_table(double const cal_table_in[XY],
CameraMode const &camera_mode, double cal_table_out[XY])
{
// Precalculate and cache the x sampling locations and phases to save
// recomputing them on every row.
int x_lo[X], x_hi[X];
double xf[X];
double scale_x = camera_mode.sensor_width /
(camera_mode.width * camera_mode.scale_x);
double x_off = camera_mode.crop_x / (double)camera_mode.sensor_width;
double x = .5 / scale_x + x_off * X - .5;
double x_inc = 1 / scale_x;
for (int i = 0; i < X; i++, x += x_inc) {
x_lo[i] = floor(x);
xf[i] = x - x_lo[i];
x_hi[i] = std::min(x_lo[i] + 1, X - 1);
x_lo[i] = std::max(x_lo[i], 0);
}
// Now march over the output table generating the new values.
double scale_y = camera_mode.sensor_height /
(camera_mode.height * camera_mode.scale_y);
double y_off = camera_mode.crop_y / (double)camera_mode.sensor_height;
double y = .5 / scale_y + y_off * Y - .5;
double y_inc = 1 / scale_y;
for (int j = 0; j < Y; j++, y += y_inc) {
int y_lo = floor(y);
double yf = y - y_lo;
int y_hi = std::min(y_lo + 1, Y - 1);
y_lo = std::max(y_lo, 0);
double const *row_above = cal_table_in + X * y_lo;
double const *row_below = cal_table_in + X * y_hi;
for (int i = 0; i < X; i++) {
double above = row_above[x_lo[i]] * (1 - xf[i]) +
row_above[x_hi[i]] * xf[i];
double below = row_below[x_lo[i]] * (1 - xf[i]) +
row_below[x_hi[i]] * xf[i];
*(cal_table_out++) = above * (1 - yf) + below * yf;
}
}
}
// Calculate chrominance statistics (R/G and B/G) for each region.
static_assert(XY == AWB_REGIONS, "ALSC/AWB statistics region mismatch");
static void calculate_Cr_Cb(bcm2835_isp_stats_region *awb_region, double Cr[XY],
double Cb[XY], uint32_t min_count, uint16_t min_G)
{
for (int i = 0; i < XY; i++) {
bcm2835_isp_stats_region &zone = awb_region[i];
if (zone.counted <= min_count ||
zone.g_sum / zone.counted <= min_G) {
Cr[i] = Cb[i] = INSUFFICIENT_DATA;
continue;
}
Cr[i] = zone.r_sum / (double)zone.g_sum;
Cb[i] = zone.b_sum / (double)zone.g_sum;
}
}
static void apply_cal_table(double const cal_table[XY], double C[XY])
{
for (int i = 0; i < XY; i++)
if (C[i] != INSUFFICIENT_DATA)
C[i] *= cal_table[i];
}
void compensate_lambdas_for_cal(double const cal_table[XY],
double const old_lambdas[XY],
double new_lambdas[XY])
{
double min_new_lambda = std::numeric_limits<double>::max();
for (int i = 0; i < XY; i++) {
new_lambdas[i] = old_lambdas[i] * cal_table[i];
min_new_lambda = std::min(min_new_lambda, new_lambdas[i]);
}
for (int i = 0; i < XY; i++)
new_lambdas[i] /= min_new_lambda;
}
static void print_cal_table(double const C[XY])
{
printf("table: [\n");
for (int j = 0; j < Y; j++) {
for (int i = 0; i < X; i++) {
printf("%5.3f", 1.0 / C[j * X + i]);
if (i != X - 1 || j != Y - 1)
printf(",");
}
printf("\n");
}
printf("]\n");
}
// Compute weight out of 1.0 which reflects how similar we wish to make the
// colours of these two regions.
static double compute_weight(double C_i, double C_j, double sigma)
{
if (C_i == INSUFFICIENT_DATA || C_j == INSUFFICIENT_DATA)
return 0;
double diff = (C_i - C_j) / sigma;
return exp(-diff * diff / 2);
}
// Compute all weights.
static void compute_W(double const C[XY], double sigma, double W[XY][4])
{
for (int i = 0; i < XY; i++) {
// Start with neighbour above and go clockwise.
W[i][0] = i >= X ? compute_weight(C[i], C[i - X], sigma) : 0;
W[i][1] = i % X < X - 1 ? compute_weight(C[i], C[i + 1], sigma)
: 0;
W[i][2] =
i < XY - X ? compute_weight(C[i], C[i + X], sigma) : 0;
W[i][3] = i % X ? compute_weight(C[i], C[i - 1], sigma) : 0;
}
}
// Compute M, the large but sparse matrix such that M * lambdas = 0.
static void construct_M(double const C[XY], double const W[XY][4],
double M[XY][4])
{
double epsilon = 0.001;
for (int i = 0; i < XY; i++) {
// Note how, if C[i] == INSUFFICIENT_DATA, the weights will all
// be zero so the equation is still set up correctly.
int m = !!(i >= X) + !!(i % X < X - 1) + !!(i < XY - X) +
!!(i % X); // total number of neighbours
// we'll divide the diagonal out straight away
double diagonal =
(epsilon + W[i][0] + W[i][1] + W[i][2] + W[i][3]) *
C[i];
M[i][0] = i >= X ? (W[i][0] * C[i - X] + epsilon / m * C[i]) /
diagonal
: 0;
M[i][1] = i % X < X - 1
? (W[i][1] * C[i + 1] + epsilon / m * C[i]) /
diagonal
: 0;
M[i][2] = i < XY - X
? (W[i][2] * C[i + X] + epsilon / m * C[i]) /
diagonal
: 0;
M[i][3] = i % X ? (W[i][3] * C[i - 1] + epsilon / m * C[i]) /
diagonal
: 0;
}
}
// In the compute_lambda_ functions, note that the matrix coefficients for the
// left/right neighbours are zero down the left/right edges, so we don't need
// need to test the i value to exclude them.
static double compute_lambda_bottom(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + X] +
M[i][3] * lambda[i - 1];
}
static double compute_lambda_bottom_start(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + X];
}
static double compute_lambda_interior(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][0] * lambda[i - X] + M[i][1] * lambda[i + 1] +
M[i][2] * lambda[i + X] + M[i][3] * lambda[i - 1];
}
static double compute_lambda_top(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][0] * lambda[i - X] + M[i][1] * lambda[i + 1] +
M[i][3] * lambda[i - 1];
}
static double compute_lambda_top_end(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][0] * lambda[i - X] + M[i][3] * lambda[i - 1];
}
// Gauss-Seidel iteration with over-relaxation.
static double gauss_seidel2_SOR(double const M[XY][4], double omega,
double lambda[XY])
{
double old_lambda[XY];
for (int i = 0; i < XY; i++)
old_lambda[i] = lambda[i];
int i;
lambda[0] = compute_lambda_bottom_start(0, M, lambda);
for (i = 1; i < X; i++)
lambda[i] = compute_lambda_bottom(i, M, lambda);
for (; i < XY - X; i++)
lambda[i] = compute_lambda_interior(i, M, lambda);
for (; i < XY - 1; i++)
lambda[i] = compute_lambda_top(i, M, lambda);
lambda[i] = compute_lambda_top_end(i, M, lambda);
// Also solve the system from bottom to top, to help spread the updates
// better.
lambda[i] = compute_lambda_top_end(i, M, lambda);
for (i = XY - 2; i >= XY - X; i--)
lambda[i] = compute_lambda_top(i, M, lambda);
for (; i >= X; i--)
lambda[i] = compute_lambda_interior(i, M, lambda);
for (; i >= 1; i--)
lambda[i] = compute_lambda_bottom(i, M, lambda);
lambda[0] = compute_lambda_bottom_start(0, M, lambda);
double max_diff = 0;
for (int i = 0; i < XY; i++) {
lambda[i] = old_lambda[i] + (lambda[i] - old_lambda[i]) * omega;
if (fabs(lambda[i] - old_lambda[i]) > fabs(max_diff))
max_diff = lambda[i] - old_lambda[i];
}
return max_diff;
}
// Normalise the values so that the smallest value is 1.
static void normalise(double *ptr, size_t n)
{
double minval = ptr[0];
for (size_t i = 1; i < n; i++)
minval = std::min(minval, ptr[i]);
for (size_t i = 0; i < n; i++)
ptr[i] /= minval;
}
static void run_matrix_iterations(double const C[XY], double lambda[XY],
double const W[XY][4], double omega,
int n_iter, double threshold)
{
double M[XY][4];
construct_M(C, W, M);
double last_max_diff = std::numeric_limits<double>::max();
for (int i = 0; i < n_iter; i++) {
double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda));
if (max_diff < threshold) {
RPI_LOG("Stop after " << i + 1 << " iterations");
break;
}
// this happens very occasionally (so make a note), though
// doesn't seem to matter
if (max_diff > last_max_diff)
RPI_LOG("Iteration " << i << ": max_diff gone up "
<< last_max_diff << " to "
<< max_diff);
last_max_diff = max_diff;
}
// We're going to normalise the lambdas so the smallest is 1. Not sure
// this is really necessary as they get renormalised later, but I
// suppose it does stop these quantities from wandering off...
normalise(lambda, XY);
}
static void add_luminance_rb(double result[XY], double const lambda[XY],
double const luminance_lut[XY],
double luminance_strength)
{
for (int i = 0; i < XY; i++)
result[i] = lambda[i] *
((luminance_lut[i] - 1) * luminance_strength + 1);
}
static void add_luminance_g(double result[XY], double lambda,
double const luminance_lut[XY],
double luminance_strength)
{
for (int i = 0; i < XY; i++)
result[i] = lambda *
((luminance_lut[i] - 1) * luminance_strength + 1);
}
void add_luminance_to_tables(double results[3][Y][X], double const lambda_r[XY],
double lambda_g, double const lambda_b[XY],
double const luminance_lut[XY],
double luminance_strength)
{
add_luminance_rb((double *)results[0], lambda_r, luminance_lut,
luminance_strength);
add_luminance_g((double *)results[1], lambda_g, luminance_lut,
luminance_strength);
add_luminance_rb((double *)results[2], lambda_b, luminance_lut,
luminance_strength);
normalise((double *)results, 3 * XY);
}
void Alsc::doAlsc()
{
double Cr[XY], Cb[XY], Wr[XY][4], Wb[XY][4], cal_table_r[XY],
cal_table_b[XY], cal_table_tmp[XY];
// Calculate our R/B ("Cr"/"Cb") colour statistics, and assess which are
// usable.
calculate_Cr_Cb(statistics_, Cr, Cb, config_.min_count, config_.min_G);
// Fetch the new calibrations (if any) for this CT. Resample them in
// case the camera mode is not full-frame.
get_cal_table(ct_, config_.calibrations_Cr, cal_table_tmp);
resample_cal_table(cal_table_tmp, async_camera_mode_, cal_table_r);
get_cal_table(ct_, config_.calibrations_Cb, cal_table_tmp);
resample_cal_table(cal_table_tmp, async_camera_mode_, cal_table_b);
// You could print out the cal tables for this image here, if you're
// tuning the algorithm...
(void)print_cal_table;
// Apply any calibration to the statistics, so the adaptive algorithm
// makes only the extra adjustments.
apply_cal_table(cal_table_r, Cr);
apply_cal_table(cal_table_b, Cb);
// Compute weights between zones.
compute_W(Cr, config_.sigma_Cr, Wr);
compute_W(Cb, config_.sigma_Cb, Wb);
// Run Gauss-Seidel iterations over the resulting matrix, for R and B.
run_matrix_iterations(Cr, lambda_r_, Wr, config_.omega, config_.n_iter,
config_.threshold);
run_matrix_iterations(Cb, lambda_b_, Wb, config_.omega, config_.n_iter,
config_.threshold);
// Fold the calibrated gains into our final lambda values. (Note that on
// the next run, we re-start with the lambda values that don't have the
// calibration gains included.)
compensate_lambdas_for_cal(cal_table_r, lambda_r_, async_lambda_r_);
compensate_lambdas_for_cal(cal_table_b, lambda_b_, async_lambda_b_);
// Fold in the luminance table at the appropriate strength.
add_luminance_to_tables(async_results_, async_lambda_r_, 1.0,
async_lambda_b_, config_.luminance_lut,
config_.luminance_strength);
}
// Register algorithm with the system.
static Algorithm *Create(Controller *controller)
{
return (Algorithm *)new Alsc(controller);
}
static RegisterAlgorithm reg(NAME, &Create);